A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer

Abstract

Purpose: Clinical trials reported 25% to 30% pathologic complete response (pCR) rates in HER2+ patients with breast cancer treated with anti-HER2 therapies without chemotherapy. We hypothesize that a multiparameter classifier can identify patients with HER2-"addicted" tumors who may benefit from a chemotherapy-sparing strategy.

Experimental design: Baseline HER2+ breast cancer specimens from the TBCRC023 and PAMELA trials, which included neoadjuvant treatment with lapatinib and trastuzumab, were used. In the case of estrogen receptor-positive (ER+) tumors, endocrine therapy was also administered. HER2 protein and gene amplification (ratio), HER2-enriched (HER2-E), and PIK3CA mutation status were assessed by dual gene protein assay (GPA), research-based PAM50, and targeted DNA-sequencing. GPA cutoffs and classifier of response were constructed in TBCRC023 using a decision tree algorithm, then validated in PAMELA.

Results: In TBCRC023, 72 breast cancer specimens had GPA, PAM50, and sequencing data, of which 15 had pCR. Recursive partitioning identified cutoffs of HER2 ratio ≥ 4.6 and %3+ IHC staining ≥ 97.5%. With PAM50 and sequencing data, the model added HER2-E and PIK3CA wild-type (WT). For clinical implementation, the classifier was locked as HER2 ratio ≥ 4.5, %3+ IHC staining ≥ 90%, and PIK3CA-WT and HER2-E, yielding 55% and 94% positive (PPV) and negative (NPV) predictive values, respectively. Independent validation using 44 PAMELA cases with all three biomarkers yielded 47% PPV and 82% NPV. Importantly, our classifier's high NPV signifies its strength in accurately identifying patients who may not be good candidates for treatment deescalation.

Conclusions: Our multiparameter classifier differentially identifies patients who may benefit from HER2-targeted therapy alone from those who need chemotherapy and predicts pCR to anti-HER2 therapy alone comparable with chemotherapy plus dual anti-HER2 therapy in unselected patients.

Figure 1.. Distribution of HER2 gene ratio and protein in TBCRC023 and PAMELA neoadjuvant cohorts.

Dot plots showing the distribution of HER2 gene ratio and percentage of HER2 3+ protein staining measured using the dual Gene Protein Assay in evaluable baseline specimens of the (A) TBCRC023 and (B) PAMELA cohort. Each circle represents an individual specimen.

Figure 2.. Pathogenic genetic alterations in TBCRC023 and PAMELA specimens.

Stacked bar graphs showing the percentage of pathogenic genetic alterations detected in the PIK3CA gene, PI3K pathway, or PI3K pathway+receptor tyrosine kinases (RTKs) in the evaluable specimens of the TBCRC023 and PAMELA cohorts.

Figure 3.. Decision tree algorithm to construct the multiparameter classifier of response.

Decision tree algorithms constructed using evaluable specimens in the TBCRC023 cohort with data for all three biomarkers show the components that make up the (A) empirical molecular classifier and (B) molecular classifier. The top tier represents the trunk of the tree with the total number of evaluable specimens and a breakdown of the pCR and non-pCR cases. The branches lead to decision nodes that contain a splitting attribute. Ovals on the left reflect cases that fit the respective splitting attribute and are therefore passed on to subsequent nodes for further selection. The white rectangle at the bottom left defines the terminal node of the tree and the final criteria of the classifier. Gray rectangles on the right reflect cases that did not meet the splitting criteria and therefore fell off the tree.